PICO
Large data samples of catchments offer insights into the physiographic and hydroclimatic factors that shape hydrological processes. These datasets increasingly encompass a diverse range of hydrologic conditions, across time and space, facilitating research on a wide variety of topics. This includes testing hypotheses of hydrologic theories, exploring uncertainties in data and models, evaluating interactions within different hydrological model structures, and enabling predictions in ungauged basins.
We welcome abstracts that seek to accelerate progress on the following topics:
1. Development and improvement of large-sample data sets:
How can we address current challenges on the uneven geographical representation of catchments, quantification of uncertainty, catchment heterogeneities and human interventions for fair comparisons among datasets? Can we foster the harmonisation of large-sample data sets? How can we test the representativeness of the available samples? How can we (systematically) represent human influences in large sample datasets?
2. Increase our process understanding:
How can we use large samples of catchments to transfer hydrologic theories (i.e. structural understanding) from well-monitored or experimental catchments to data-scarce catchments? Can currently available global datasets be used to draw improved perceptual models and better define hydrologic similarity?
3.Advance catchment modelling:
How can we improve process-based and machine learning modelling by using large samples of catchments? How can information and knowledge (i.e. functional understanding) be transferred between catchments and applied to data-scarce regions? Furthermore, how can we develop new models and workflows to more effectively leverage these models to infer hydrological response under changing environmental conditions, particularly those influenced by human activities?
4. Hydrologic synthesis:
How can we use catchment descriptors available in large sample datasets to infer dominant controls for relevant hydrological processes? Do we need the definition of new catchment descriptors or the inclusion of new variables? How can we improve our classification of catchments, of their connectivity and of their hydrologic processes?
We welcome abstracts that seek to accelerate progress on the following topics:
1. Development and improvement of large-sample data sets:
How can we address current challenges on the uneven geographical representation of catchments, quantification of uncertainty, catchment heterogeneities and human interventions for fair comparisons among datasets? Can we foster the harmonisation of large-sample data sets? How can we test the representativeness of the available samples? How can we (systematically) represent human influences in large sample datasets?
2. Increase our process understanding:
How can we use large samples of catchments to transfer hydrologic theories (i.e. structural understanding) from well-monitored or experimental catchments to data-scarce catchments? Can currently available global datasets be used to draw improved perceptual models and better define hydrologic similarity?
3.Advance catchment modelling:
How can we improve process-based and machine learning modelling by using large samples of catchments? How can information and knowledge (i.e. functional understanding) be transferred between catchments and applied to data-scarce regions? Furthermore, how can we develop new models and workflows to more effectively leverage these models to infer hydrological response under changing environmental conditions, particularly those influenced by human activities?
4. Hydrologic synthesis:
How can we use catchment descriptors available in large sample datasets to infer dominant controls for relevant hydrological processes? Do we need the definition of new catchment descriptors or the inclusion of new variables? How can we improve our classification of catchments, of their connectivity and of their hydrologic processes?